Historically the biggest single issue involved in the usage, construction and architecture of knowledge based systems has been the question of how to extract the knowledge, expertise and human decision making capability from one or more humans, in a data format which enables the knowledge, expertise and decision making capability to be computable in a machine. The problem is of sufficient magnitude, that it is well known in the art of knowledge based systems, by the name of “the knowledge elicitation bottle neck”.
The elicitation of knowledge from human beings has proved to be a profoundly difficult problem for the information technology industry, to the extent that although great ideas have been produced for computer programs, which embody reasoning capability, the data over which they reason, that is to say the knowledge bases, have been very difficult to construct. Conventional Von Neumann architecture computers having a data processor, a memory and a data storage device require a data base having a relatively rigid architecture. However, the way in which humans think and consider problems does not apparently or clearly follow rigid logical processes. The problem of extracting human knowledge, which often appears to be unstructured and intuitive, into a computable form of data suitable for a conventional computer to process is a difficult technical problem.
Referring to FIG. 1 herein, there is illustrated schematically in diagrammatic form, the knowledge elicitation problem. The problem consists of the extraction of knowledge from a plurality of human individuals 100-103, and conversion of that knowledge into data in a format specific to a data base or knowledge base 104 of a computer 105, in which format the data can be stored directly in the data base, having been entered through an interface 106 of the computer. The data is stored in the data base under control of a processor 107 and memory 108 of a computer.
Referring to FIG. 2 herein, there is illustrated schematically prior art processes carried out for solving the knowledge elicitation problem. The processes shown in FIG. 2 are generic to a range of prior art solutions for capture of knowledge within prior art expert systems. In general, prior art knowledge elicitation processes comprise a knowledge capture process 200 comprising the stages of problem identification, in which a problem to be solved by an expert system is identified;                a knowledge flow identification process;        a knowledge source identification process, in which one or a plurality of experts are identified who can give the relevant information; and        actual storage 201 of electronic data in known positions in the data storage device, i. e. within an electronically accessible data base, where the electronic data stored represents knowledge which has been captured from one or more individual experts.        
Problems occur in the knowledge capture process 200, in that complete and full knowledge may not be extracted by prior art questioning process. Problems also occur in the arrangement of data into a form suitable for input into a prior art data base architecture, because the data base architecture may not be flexible enough to contain all the complex inter relationships between facts and statements comprising the knowledge.
Prior art database architectures often only allow for one mode of inference over the data contained therein, and do not demand that consistency of the data contained in the database is proved. It is possible for inconsistent information and knowledge to be stored in or introduced into a prior art database.
The inventors have recognized that the vast majority of prior art solutions addressing the knowledge elicitation bottleneck take a psychology based approach, comprising various different methods of asking the expert what they do, recording that information, and producing unstructured text, grids and matrices and applying different types of statistical manipulation to the data in an attempt to derive production rules from the data. That is, prior solutions attempt to produce standard computer understandable clauses, such as IF, THEN, and WHEN rules, in order to establish a rule base. Conventional rule bases, for example PROLOG rule bases, are generally a subset of predicate or propositional calculus, and most prior art knowledge based systems ultimately store their data according to such rule bases.
The inventors have realized that whilst a psychology based approach results in a rich conceptual map of how an expert is thinking in a knowledge domain, it does not actually give a rule base, and the knowledge elicited from a human expert using prior art logic and psychology based methods does not result in directly computable data. In prior art methods there needs to be applied much heavy thought about the unstructured text, information, matrices and grids, in order to try and derive some rules from them, and input them into a computer system, typically finding that this does not work fully, and then having to run known consistency checks because the rule base does not work, followed by a return to the expert for further questioning and re-eliciting knowledge from the expert, repeatedly manipulating the elicited data (sometimes including the application of statistical methods), in a potentially endless iterative loop (although most practical knowledge elicitation processes do stop somewhere).
Prior art knowledge elicitation processes are unable to guarantee consistency or completeness of rule bases determined from those processes, and can result in hundreds or thousands of rules in a knowledge base, which need to be checked for internal self consistency. Prior art systems may result in rule bases where mutually incompatible rules exist within the rule base.
Consequently, prior art knowledge elicitation processes have the major disadvantage that because of the deficiencies of the prior art elicitation processes, the persons carrying out the elicitation of knowledge from human experts, in order to be sufficiently effective, tend to need to become experts themselves. These knowledge engineers would typically require a long training process in order to know what questions to ask a knowledge expert, thus resulting in a large requirement in time, cost and resources for creating knowledge based systems and expert systems.
Problems with the prior art approaches include the fact that often only the result of the rules is elicited, not the rules themselves, and additionally the human expert only recites their conscious knowledge. In the prior art logic and psychology based knowledge elicitation approaches, only the conscious knowledge is addressed.
Consequently, the inventors have realized that prior art logic and psychology based knowledge elicitation processes singularly fail, to a greater or lesser extent, to address sub-conscious knowledge, internal consistency, interrelations, dependencies, or the cognitive explanation of these, and consequently do not overcome the knowledge elicitation bottleneck.